In a multi-user multiple-input multiple-output (MIMO) uplink channel, the presence of multiple receive antennas enables a base station to serve a number of users simultaneously, thus increasing overall system throughput. In practice, users have to be scheduled instead of being served all at once. In the MIMO uplink channel, several transmitters (e.g. user terminals) send data to a single receiver (e.g. base station) simultaneously. Such a concurrent transmission is enabled by the presence of multiple antennas at the base station, which can apply spatial processing to separate data streams. Having multiple antennas, the base station is able to receive data streams from several users concurrently by exploiting the spatial properties of the transmission channel, resulting in a significantly enhanced system throughput. The concept of serving several users at the same resource is sometimes also referred to as “Virtual MIMO”.
A practical problem in this situation is the difficulty of selecting a suitably small user subset for simultaneous transmission (multi-user scheduling). The number of simultaneously decodable independent data streams is in practice limited by the number of receive antennas at the base station; therefore, it is usually not possible to serve all the users in the same resource unit. Instead, a subset of users has to be selected by the base station for simultaneous transmission. A spatial compatibility metric (scheduling metric) is used for evaluating which users can be beneficially grouped together. Based on a scheduling metric, a scheduling algorithm is employed which checks certain candidate groupings via their respective compatibility metric value, and it finally makes a scheduling decision. A straight-forward scheduling algorithm is an exhaustive search which probes all possible user combinations and picks the one with the best scheduling metric. However, this approach is often practically infeasible due to its excessive computational complexity.
Suboptimal scheduling algorithms aiming at reduced complexity have been proposed, e.g., based on sequential scheduling, simulated annealing, and a tree data structure. As a scheduling metric, instantaneous channel capacity, resulting SINR, or related measures such as the minimal transmit power necessary for achieving a given target performance have been proposed. However, these known approaches are based on instantaneous channel knowledge, such as SINR, or capacity used for calculating the scheduling metric. This makes the amount of required channel sounding at each transmission band excessive at the scheduling stage, thus increasing the usage of radio system resources.